Alessandro Vinceti
- Postdoc, Iorio Group
Alessandro Vinceti is a PhD student in the Computational Biology Research Centre at Human Technopole. He completed a Bachelor’s in Biotechnology from the University of Modena and Reggio Emilia, followed by a Master’s in Bioinformatics from the University of Bologna.
As part of his PhD, Alessandro is contributing to the development of tools for the correction and analysis of data derived from CRISPR-Cas9 screens based on large cohorts of human immortalised cancer cell lines. The aim is to exploit the genetic features of tumour models to find suitable therapeutic targets that will lead to the development/repurposing of the right drug for the right patient, following precision medicine principles.
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Publications
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07/2024 - Genome Biology
A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data
Background CRISPR-Cas9 dropout screens are formidable tools for investigating biology with unprecedented precision and scale. However, biases in data lead to potential confounding effects on interpretation and compromise overall quality. The activity of Cas9 is influenced by structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend […]
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01/2023 - Bioinformatics
A heuristic algorithm solving the mutual-exclusivity sorting problem
Motivation Binary (or boolean) matrices provide a common effective data representation adopted in several domains of computational biology, especially for investigating cancer and other human diseases. For instance, they are used to summarise genetic aberrations—copy number alterations or mutations—observed in cancer patient cohorts, effectively highlighting combinatorial relations among them. One of these is the tendency […]
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01/2023 - Cell Reports Methods
An interactive web application for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens
A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing […]
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10/2022 - Nature
Phenotypic plasticity and genetic control in colorectal cancer evolution
Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, […]
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07/2022 - Cell Reports
Reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 fitness screens
Pooled genome-wide CRISPR-Cas9 screens are furthering our mechanistic understanding of human biology and have allowed us to identify new oncology therapeutic targets. Scale-limited CRISPR-Cas9 screens—typically employing guide RNA libraries targeting subsets of functionally related genes, biological pathways, or portions of the druggable genome—constitute an optimal setting for investigating narrow hypotheses and are easier to execute […]
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